Overview

Dataset statistics

Number of variables33
Number of observations2126
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)0.5%
Total size in memory548.2 KiB
Average record size in memory264.1 B

Variable types

Numeric21
Categorical12

Warnings

Dataset has 11 (0.5%) duplicate rows Duplicates
Mode is highly correlated with MedianHigh correlation
Mean is highly correlated with MedianHigh correlation
Median is highly correlated with Mode and 1 other fieldsHigh correlation
MSTV has 47 (2.2%) zeros Zeros
ALTV has 1240 (58.3%) zeros Zeros
MLTV has 137 (6.4%) zeros Zeros
Min has 77 (3.6%) zeros Zeros
Nmax has 107 (5.0%) zeros Zeros
Nzeros has 1624 (76.4%) zeros Zeros
Variance has 187 (8.8%) zeros Zeros
nAC has 891 (41.9%) zeros Zeros
nFM has 1311 (61.7%) zeros Zeros
nUC has 332 (15.6%) zeros Zeros
nDL has 1231 (57.9%) zeros Zeros
nDS has 2119 (99.7%) zeros Zeros
nDP has 1948 (91.6%) zeros Zeros

Reproduction

Analysis started2022-02-19 05:04:57.946363
Analysis finished2022-02-19 05:05:33.157776
Duration35.21 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

LB
Real number (ℝ≥0)

Distinct57
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5056269818
Minimum0
Maximum1
Zeros7
Zeros (%)0.3%
Memory size16.7 KiB
2022-02-19T00:05:33.228023image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2407407407
Q10.3703703704
median0.5
Q30.6296296296
95-th percentile0.7962962963
Maximum1
Range1
Interquartile range (IQR)0.2592592593

Descriptive statistics

Standard deviation0.1822378563
Coefficient of variation (CV)0.3604195639
Kurtosis-0.2929428996
Mean0.5056269818
Median Absolute Deviation (MAD)0.1296296296
Skewness0.02031219158
Sum1074.962963
Variance0.03321063627
MonotocityNot monotonic
2022-02-19T00:05:33.314879image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5136
 
6.4%
0.2962962963109
 
5.1%
0.5925925926103
 
4.8%
0.444444444493
 
4.4%
0.259259259378
 
3.7%
0.703703703777
 
3.6%
0.666666666776
 
3.6%
0.481481481576
 
3.6%
0.407407407468
 
3.2%
0.518518518567
 
3.2%
Other values (47)1243
58.5%
ValueCountFrequency (%)
07
 
0.3%
0.0740740740721
1.0%
0.111111111116
0.8%
0.148148148111
 
0.5%
0.166666666728
1.3%
ValueCountFrequency (%)
11
 
< 0.1%
0.981481481512
0.6%
0.96296296310
0.5%
0.94444444444
 
0.2%
0.92592592594
 
0.2%

ASTV
Real number (ℝ≥0)

Distinct92
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.466534964
Minimum0
Maximum1
Zeros2
Zeros (%)0.1%
Memory size16.7 KiB
2022-02-19T00:05:33.411434image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.2666666667
median0.4933333333
Q30.6533333333
95-th percentile0.84
Maximum1
Range1
Interquartile range (IQR)0.3866666667

Descriptive statistics

Standard deviation0.2292375162
Coefficient of variation (CV)0.4913619212
Kurtosis-1.051029578
Mean0.466534964
Median Absolute Deviation (MAD)0.1866666667
Skewness-0.01182857864
Sum991.8533334
Variance0.05254983885
MonotocityNot monotonic
2022-02-19T00:05:33.511322image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6462
 
2.9%
0.613333333359
 
2.8%
0.6858
 
2.7%
0.653333333355
 
2.6%
0.5254
 
2.5%
0.693333333350
 
2.4%
0.133333333348
 
2.3%
0.173333333346
 
2.2%
0.666666666745
 
2.1%
0.293333333343
 
2.0%
Other values (82)1606
75.5%
ValueCountFrequency (%)
02
 
0.1%
0.013333333337
0.3%
0.026666666674
 
0.2%
0.044
 
0.2%
0.0533333333312
0.6%
ValueCountFrequency (%)
11
 
< 0.1%
0.98666666674
0.2%
0.966
0.3%
0.94666671
 
< 0.1%
0.94666666673
0.1%

MSTV
Real number (ℝ≥0)

ZEROS

Distinct90
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1665859663
Minimum0
Maximum1
Zeros47
Zeros (%)2.2%
Memory size16.7 KiB
2022-02-19T00:05:33.602602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01470588235
Q10.07352941176
median0.1470588235
Q30.2205882353
95-th percentile0.4117647
Maximum1
Range1
Interquartile range (IQR)0.1470588235

Descriptive statistics

Standard deviation0.1298884314
Coefficient of variation (CV)0.7797081249
Kurtosis4.70075636
Mean0.1665859663
Median Absolute Deviation (MAD)0.07352941176
Skewness1.657339207
Sum354.1617644
Variance0.01687100462
MonotocityNot monotonic
2022-02-19T00:05:33.699253image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08823529412120
 
5.6%
0.04411764706117
 
5.5%
0.1617647059113
 
5.3%
0.02941176471109
 
5.1%
0.1029411765108
 
5.1%
0.05882352941107
 
5.0%
0.07352941176106
 
5.0%
0.1470588235100
 
4.7%
0.191176470699
 
4.7%
0.117647058895
 
4.5%
Other values (80)1052
49.5%
ValueCountFrequency (%)
047
2.2%
0.014705882
 
0.1%
0.0147058823582
3.9%
0.0294117611
 
0.5%
0.02941176471109
5.1%
ValueCountFrequency (%)
11
< 0.1%
0.98529411761
< 0.1%
0.89705882352
0.1%
0.85294117651
< 0.1%
0.83823529411
< 0.1%

ALTV
Real number (ℝ≥0)

ZEROS

Distinct128
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1082050592
Minimum0
Maximum1
Zeros1240
Zeros (%)58.3%
Memory size16.7 KiB
2022-02-19T00:05:33.784981image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1208791209
95-th percentile0.6153846154
Maximum1
Range1
Interquartile range (IQR)0.1208791209

Descriptive statistics

Standard deviation0.2021635129
Coefficient of variation (CV)1.868336973
Kurtosis4.252997856
Mean0.1082050592
Median Absolute Deviation (MAD)0
Skewness2.195075309
Sum230.0439559
Variance0.04087008593
MonotocityNot monotonic
2022-02-19T00:05:33.881138image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01240
58.3%
0.0109890109950
 
2.4%
0.0219780219845
 
2.1%
0.0439560439639
 
1.8%
0.0549450549538
 
1.8%
0.0329670329736
 
1.7%
0.0879120879132
 
1.5%
0.0659340659328
 
1.3%
0.131868131927
 
1.3%
0.0769230769223
 
1.1%
Other values (118)568
26.7%
ValueCountFrequency (%)
01240
58.3%
0.010989012
 
0.1%
0.0109890109950
 
2.4%
0.0219780219845
 
2.1%
0.0329670329736
 
1.7%
ValueCountFrequency (%)
14
0.2%
0.9890109892
0.1%
0.9670329671
 
< 0.1%
0.94505494511
 
< 0.1%
0.93406593411
 
< 0.1%

MLTV
Real number (ℝ≥0)

ZEROS

Distinct302
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1614917032
Minimum0
Maximum1
Zeros137
Zeros (%)6.4%
Memory size16.7 KiB
2022-02-19T00:05:33.977561image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09072978304
median0.1459566075
Q30.2130177515
95-th percentile0.3643984221
Maximum1
Range1
Interquartile range (IQR)0.1222879684

Descriptive statistics

Standard deviation0.1110107811
Coefficient of variation (CV)0.6874085722
Kurtosis4.131253849
Mean0.1614917032
Median Absolute Deviation (MAD)0.06114398422
Skewness1.331997908
Sum343.331361
Variance0.01232339353
MonotocityNot monotonic
2022-02-19T00:05:34.084686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0137
 
6.4%
0.140039447727
 
1.3%
0.132149901427
 
1.3%
0.102564102624
 
1.1%
0.128205128224
 
1.1%
0.187376725824
 
1.1%
0.13412228822
 
1.0%
0.110453648922
 
1.0%
0.1676528622
 
1.0%
0.159763313621
 
1.0%
Other values (292)1776
83.5%
ValueCountFrequency (%)
0137
6.4%
0.0019723865884
 
0.2%
0.0039447731764
 
0.2%
0.0059171597637
 
0.3%
0.005917162
 
0.1%
ValueCountFrequency (%)
11
< 0.1%
0.82445759371
< 0.1%
0.80473372781
< 0.1%
0.72781065091
< 0.1%
0.70414201181
< 0.1%

Width
Real number (ℝ≥0)

Distinct199
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3810503263
Minimum0
Maximum1
Zeros2
Zeros (%)0.1%
Memory size16.7 KiB
2022-02-19T00:05:34.198989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07344632768
Q10.1920903955
median0.3644067605
Q30.5480225989
95-th percentile0.7627118644
Maximum1
Range1
Interquartile range (IQR)0.3559322034

Descriptive statistics

Standard deviation0.2200886606
Coefficient of variation (CV)0.5775842335
Kurtosis-0.9022867775
Mean0.3810503263
Median Absolute Deviation (MAD)0.1779661017
Skewness0.3142347545
Sum810.1129937
Variance0.04843901854
MonotocityNot monotonic
2022-02-19T00:05:34.292514image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.203389830542
 
2.0%
0.559322033935
 
1.6%
0.536723163828
 
1.3%
0.451977401127
 
1.3%
0.135593220326
 
1.2%
0.107344632825
 
1.2%
0.276836158225
 
1.2%
0.220338983124
 
1.1%
0.192090395523
 
1.1%
0.175141242923
 
1.1%
Other values (189)1848
86.9%
ValueCountFrequency (%)
02
 
0.1%
0.011299435032
 
0.1%
0.016949152541
 
< 0.1%
0.022598870063
 
0.1%
0.0282485875710
0.5%
ValueCountFrequency (%)
11
 
< 0.1%
0.97740112996
0.3%
0.90395480232
 
0.1%
0.89830508471
 
< 0.1%
0.89265536725
0.2%

Min
Real number (ℝ≥0)

ZEROS

Distinct142
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3998118534
Minimum0
Maximum1
Zeros77
Zeros (%)3.6%
Memory size16.7 KiB
2022-02-19T00:05:34.382753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.009174311927
Q10.1559633028
median0.3944954128
Q30.6422018349
95-th percentile0.8165137615
Maximum1
Range1
Interquartile range (IQR)0.4862385321

Descriptive statistics

Standard deviation0.2711946073
Coefficient of variation (CV)0.6783055704
Kurtosis-1.29042219
Mean0.3998118534
Median Absolute Deviation (MAD)0.2477063872
Skewness0.1157840239
Sum850.0000003
Variance0.07354651501
MonotocityNot monotonic
2022-02-19T00:05:34.479770image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
077
 
3.6%
0.0183486238550
 
2.4%
0.192660550546
 
2.2%
0.0917431192745
 
2.1%
0.165137614740
 
1.9%
0.642201834939
 
1.8%
0.155963302838
 
1.8%
0.486238532137
 
1.7%
0.00917431192736
 
1.7%
0.110091743135
 
1.6%
Other values (132)1683
79.2%
ValueCountFrequency (%)
077
3.6%
0.00917431192736
1.7%
0.0183486238550
2.4%
0.0275229357832
1.5%
0.0366972477127
 
1.3%
ValueCountFrequency (%)
11
 
< 0.1%
0.99082568811
 
< 0.1%
0.97247706421
 
< 0.1%
0.96330275232
0.1%
0.95412844043
0.1%

Max
Real number (ℝ≥0)

Distinct114
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3622879297
Minimum0
Maximum1
Zeros2
Zeros (%)0.1%
Memory size16.7 KiB
2022-02-19T00:05:34.601251image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1379310345
Q10.2586206897
median0.3448275862
Q30.4482758621
95-th percentile0.6551724138
Maximum1
Range1
Interquartile range (IQR)0.1896551724

Descriptive statistics

Standard deviation0.1546912338
Coefficient of variation (CV)0.4269842329
Kurtosis0.6327695058
Mean0.3622879297
Median Absolute Deviation (MAD)0.09482758621
Skewness0.577862453
Sum770.2241385
Variance0.0239293778
MonotocityNot monotonic
2022-02-19T00:05:34.697594image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.301724137967
 
3.2%
0.422413793161
 
2.9%
0.293103448359
 
2.8%
0.318965517256
 
2.6%
0.482758620752
 
2.4%
0.27586206951
 
2.4%
0.258620689750
 
2.4%
0.310344827650
 
2.4%
0.431034482848
 
2.3%
0.370689655248
 
2.3%
Other values (104)1584
74.5%
ValueCountFrequency (%)
02
 
0.1%
0.0086206896552
 
0.1%
0.025862068973
0.1%
0.034482758625
0.2%
0.043103448282
 
0.1%
ValueCountFrequency (%)
16
0.3%
0.93103453
0.1%
0.91379310345
0.2%
0.78448275861
 
< 0.1%
0.76724137935
0.2%

Nmax
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2260112886
Minimum0
Maximum1
Zeros107
Zeros (%)5.0%
Memory size16.7 KiB
2022-02-19T00:05:34.778344image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01388888889
Q10.1111111111
median0.1666667
Q30.3333333333
95-th percentile0.5555555556
Maximum1
Range1
Interquartile range (IQR)0.2222222222

Descriptive statistics

Standard deviation0.1638547566
Coefficient of variation (CV)0.7249848345
Kurtosis0.5042105483
Mean0.2260112886
Median Absolute Deviation (MAD)0.1111111
Skewness0.8928859202
Sum480.4999995
Variance0.02684838127
MonotocityNot monotonic
2022-02-19T00:05:34.877600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05555555556333
15.7%
0.1111111111307
14.4%
0.1666666667261
12.3%
0.2222222222243
11.4%
0.2777777778193
9.1%
0.3333333333142
6.7%
0.3888888889138
6.5%
0107
 
5.0%
0.444444444498
 
4.6%
0.567
 
3.2%
Other values (17)237
11.1%
ValueCountFrequency (%)
0107
 
5.0%
0.05555555556333
15.7%
0.0555555624
 
1.1%
0.111111124
 
1.1%
0.1111111111307
14.4%
ValueCountFrequency (%)
11
 
< 0.1%
0.88888888892
 
0.1%
0.83333333331
 
< 0.1%
0.77777777785
0.2%
0.722222222210
0.5%

Nzeros
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03236124177
Minimum0
Maximum1
Zeros1624
Zeros (%)76.4%
Memory size16.7 KiB
2022-02-19T00:05:34.941996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07060593732
Coefficient of variation (CV)2.181805563
Kurtosis30.36508416
Mean0.03236124177
Median Absolute Deviation (MAD)0
Skewness3.920287371
Sum68.8
Variance0.004985198384
MonotocityNot monotonic
2022-02-19T00:05:35.006139image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
01624
76.4%
0.1366
 
17.2%
0.2108
 
5.1%
0.321
 
1.0%
0.42
 
0.1%
0.52
 
0.1%
11
 
< 0.1%
0.81
 
< 0.1%
0.71
 
< 0.1%
ValueCountFrequency (%)
01624
76.4%
0.1366
 
17.2%
0.2108
 
5.1%
0.321
 
1.0%
0.42
 
0.1%
ValueCountFrequency (%)
11
< 0.1%
0.81
< 0.1%
0.71
< 0.1%
0.52
0.1%
0.42
0.1%

Mode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct120
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6098584453
Minimum0
Maximum1
Zeros6
Zeros (%)0.3%
Memory size16.7 KiB
2022-02-19T00:05:35.091254image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4035433071
Q10.5433070866
median0.6220472441
Q30.6929133858
95-th percentile0.7874015748
Maximum1
Range1
Interquartile range (IQR)0.1496062992

Descriptive statistics

Standard deviation0.1289865295
Coefficient of variation (CV)0.2115024077
Kurtosis3.009530538
Mean0.6098584453
Median Absolute Deviation (MAD)0.07874015748
Skewness-0.9951778417
Sum1296.559055
Variance0.0166375248
MonotocityNot monotonic
2022-02-19T00:05:35.165636image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5748031496135
 
6.3%
0.708661417389
 
4.2%
0.645669291387
 
4.1%
0.692913385879
 
3.7%
0.661417322876
 
3.6%
0.598425196975
 
3.5%
0.653543307171
 
3.3%
0.543307086670
 
3.3%
0.511811023667
 
3.2%
0.685039370164
 
3.0%
Other values (110)1313
61.8%
ValueCountFrequency (%)
06
0.3%
0.055118110245
0.2%
0.070866141
 
< 0.1%
0.086614173231
 
< 0.1%
0.11811023626
0.3%
ValueCountFrequency (%)
11
 
< 0.1%
0.99212598436
0.3%
0.94488188984
0.2%
0.9370078741
 
< 0.1%
0.91338582686
0.3%

Mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct152
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5652342772
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Memory size16.7 KiB
2022-02-19T00:05:35.276640image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3211009174
Q10.4770642202
median0.5779816514
Q30.6605504587
95-th percentile0.7706422018
Maximum1
Range1
Interquartile range (IQR)0.1834862385

Descriptive statistics

Standard deviation0.1430605167
Coefficient of variation (CV)0.2530995067
Kurtosis0.9334275064
Mean0.5652342772
Median Absolute Deviation (MAD)0.09174311927
Skewness-0.6510192396
Sum1201.688073
Variance0.02046631144
MonotocityNot monotonic
2022-02-19T00:05:35.361833image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.651376146864
 
3.0%
0.642201834961
 
2.9%
0.62385321160
 
2.8%
0.660550458757
 
2.7%
0.568807339457
 
2.7%
0.678899082656
 
2.6%
0.541284403756
 
2.6%
0.550458715656
 
2.6%
0.614678899156
 
2.6%
0.559633027553
 
2.5%
Other values (142)1550
72.9%
ValueCountFrequency (%)
01
< 0.1%
0.018348623851
< 0.1%
0.027522935781
< 0.1%
0.045871559631
< 0.1%
0.055045871561
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.98165137611
< 0.1%
0.96330275231
< 0.1%
0.93577981651
< 0.1%
0.91743119272
0.1%

Median
Real number (ℝ≥0)

HIGH CORRELATION

Distinct132
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5604615638
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Memory size16.7 KiB
2022-02-19T00:05:35.457584image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3302752294
Q10.4770642202
median0.5688073394
Q30.6513761468
95-th percentile0.752293578
Maximum1
Range1
Interquartile range (IQR)0.1743119266

Descriptive statistics

Standard deviation0.1327209985
Coefficient of variation (CV)0.2368066021
Kurtosis0.6672593307
Mean0.5604615638
Median Absolute Deviation (MAD)0.09174311927
Skewness-0.4784141937
Sum1191.541285
Variance0.01761486344
MonotocityNot monotonic
2022-02-19T00:05:35.556728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.633027522968
 
3.2%
0.596330275268
 
3.2%
0.642201834965
 
3.1%
0.550458715661
 
2.9%
0.62385321161
 
2.9%
0.678899082661
 
2.9%
0.660550458759
 
2.8%
0.522935779858
 
2.7%
0.587155963358
 
2.7%
0.605504587256
 
2.6%
Other values (122)1511
71.1%
ValueCountFrequency (%)
01
< 0.1%
0.0091743119271
< 0.1%
0.018348623852
0.1%
0.045871559631
< 0.1%
0.082568807341
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.97247706421
< 0.1%
0.94495412841
< 0.1%
0.92660550461
< 0.1%
0.91743119271
< 0.1%

Variance
Real number (ℝ≥0)

ZEROS

Distinct164
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06991855135
Minimum0
Maximum1
Zeros187
Zeros (%)8.8%
Memory size16.7 KiB
2022-02-19T00:05:35.651356image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.007434944238
median0.02602230483
Q30.08921933086
95-th percentile0.282527881
Maximum1
Range1
Interquartile range (IQR)0.08178438662

Descriptive statistics

Standard deviation0.1077235538
Coefficient of variation (CV)1.540700598
Kurtosis15.13158929
Mean0.06991855135
Median Absolute Deviation (MAD)0.02230483271
Skewness3.219973836
Sum148.6468402
Variance0.01160436405
MonotocityNot monotonic
2022-02-19T00:05:35.748172image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003717472119219
 
10.3%
0187
 
8.8%
0.007434944238157
 
7.4%
0.01115241636147
 
6.9%
0.01486988848104
 
4.9%
0.0185873605985
 
4.0%
0.0297397769570
 
3.3%
0.0223048327165
 
3.1%
0.0260223048350
 
2.4%
0.0334572490744
 
2.1%
Other values (154)998
46.9%
ValueCountFrequency (%)
0187
8.8%
0.00371747229
 
1.4%
0.003717472119219
10.3%
0.0074349449
 
0.4%
0.007434944238157
7.4%
ValueCountFrequency (%)
11
< 0.1%
0.94423791821
< 0.1%
0.92936802971
< 0.1%
0.90334572491
< 0.1%
0.89591078071
< 0.1%

nAC
Real number (ℝ≥0)

ZEROS

Distinct1023
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1643706312
Minimum0
Maximum1
Zeros891
Zeros (%)41.9%
Memory size16.7 KiB
2022-02-19T00:05:35.828320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.08452677637
Q30.2920296809
95-th percentile0.5698587127
Maximum1
Range1
Interquartile range (IQR)0.2920296809

Descriptive statistics

Standard deviation0.2001567369
Coefficient of variation (CV)1.217715935
Kurtosis0.7843742408
Mean0.1643706312
Median Absolute Deviation (MAD)0.08452677637
Skewness1.210084644
Sum349.451962
Variance0.04006271932
MonotocityNot monotonic
2022-02-19T00:05:35.924391image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0891
41.9%
0.0432503276514
 
0.7%
0.12975098310
 
0.5%
0.17300131069
 
0.4%
0.30275229369
 
0.4%
0.25950196599
 
0.4%
0.38925294897
 
0.3%
0.086500655317
 
0.3%
0.21625163837
 
0.3%
0.43250327657
 
0.3%
Other values (1013)1156
54.4%
ValueCountFrequency (%)
0891
41.9%
0.014408764341
 
< 0.1%
0.020123066691
 
< 0.1%
0.024391882811
 
< 0.1%
0.033717258031
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.92602040821
< 0.1%
0.91620393741
< 0.1%
0.9018633541
< 0.1%
0.89925681261
< 0.1%

nFM
Real number (ℝ≥0)

ZEROS

Distinct708
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01971143092
Minimum0
Maximum1
Zeros1311
Zeros (%)61.7%
Memory size16.7 KiB
2022-02-19T00:05:36.034392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.005227084957
95-th percentile0.05885678831
Maximum1
Range1
Interquartile range (IQR)0.005227084957

Descriptive statistics

Standard deviation0.09710059489
Coefficient of variation (CV)4.926105837
Kurtosis64.26696295
Mean0.01971143092
Median Absolute Deviation (MAD)0
Skewness7.812157433
Sum41.90650213
Variance0.009428525527
MonotocityNot monotonic
2022-02-19T00:05:36.134168image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01311
61.7%
0.0017352677910
 
0.5%
0.00694107116110
 
0.5%
0.0034705355819
 
0.4%
0.012146874537
 
0.3%
0.0052058033716
 
0.3%
0.010411606745
 
0.2%
0.027764284654
 
0.2%
0.0086763389524
 
0.2%
0.031234820234
 
0.2%
Other values (698)756
35.6%
ValueCountFrequency (%)
01311
61.7%
0.0012110512691
 
< 0.1%
0.0012267606611
 
< 0.1%
0.0012906861541
 
< 0.1%
0.0015186761
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99286326641
< 0.1%
0.97869103371
< 0.1%
0.97631545651
< 0.1%
0.94734589581
< 0.1%

nUC
Real number (ℝ≥0)

ZEROS

Distinct1354
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2919055092
Minimum0
Maximum1
Zeros332
Zeros (%)15.6%
Memory size16.7 KiB
2022-02-19T00:05:36.217208image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1256742782
median0.3002802063
Q30.4371686616
95-th percentile0.6143272493
Maximum1
Range1
Interquartile range (IQR)0.3114943834

Descriptive statistics

Standard deviation0.1970014481
Coefficient of variation (CV)0.6748808839
Kurtosis-0.6494609471
Mean0.2919055092
Median Absolute Deviation (MAD)0.149824771
Skewness0.1563766149
Sum620.5911126
Variance0.03880957054
MonotocityNot monotonic
2022-02-19T00:05:36.314149image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0332
 
15.6%
0.0558798999227
 
1.3%
0.111759799816
 
0.8%
0.167639699713
 
0.6%
0.279399499611
 
0.5%
0.22351959978
 
0.4%
0.33527939957
 
0.3%
0.27235772366
 
0.3%
0.39880952386
 
0.3%
0.41104294486
 
0.3%
Other values (1344)1694
79.7%
ValueCountFrequency (%)
0332
15.6%
0.049048321
 
< 0.1%
0.055740431
 
< 0.1%
0.055833333331
 
< 0.1%
0.0558798999227
 
1.3%
ValueCountFrequency (%)
11
< 0.1%
0.96303901441
< 0.1%
0.92871287131
< 0.1%
0.87012987011
< 0.1%
0.84572230011
< 0.1%

nDL
Real number (ℝ≥0)

ZEROS

Distinct759
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1224971638
Minimum0
Maximum1
Zeros1231
Zeros (%)57.9%
Memory size16.7 KiB
2022-02-19T00:05:36.409824image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.212142074
95-th percentile0.5417239592
Maximum1
Range1
Interquartile range (IQR)0.212142074

Descriptive statistics

Standard deviation0.1925471916
Coefficient of variation (CV)1.571850201
Kurtosis2.506369273
Mean0.1224971638
Median Absolute Deviation (MAD)0
Skewness1.721570262
Sum260.4289703
Variance0.03707442101
MonotocityNot monotonic
2022-02-19T00:05:36.505810image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01231
57.9%
0.054211843222
 
1.0%
0.10842368647
 
0.3%
0.21684737285
 
0.2%
0.16263552965
 
0.2%
0.2783725914
 
0.2%
0.55084745763
 
0.1%
0.33505154643
 
0.1%
0.22336769763
 
0.1%
0.42763157893
 
0.1%
Other values (749)840
39.5%
ValueCountFrequency (%)
01231
57.9%
0.03783469151
 
< 0.1%
0.04212572911
 
< 0.1%
0.042511445391
 
< 0.1%
0.043594902751
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.95823095821
< 0.1%
0.95432300161
< 0.1%
0.93429158111
< 0.1%
0.92067988671
< 0.1%

nDS
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002649101406
Minimum0
Maximum1
Zeros2119
Zeros (%)99.7%
Memory size16.7 KiB
2022-02-19T00:05:36.570442image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04650078393
Coefficient of variation (CV)17.55341786
Kurtosis321.0847432
Mean0.002649101406
Median Absolute Deviation (MAD)0
Skewness17.81170194
Sum5.631989588
Variance0.002162322906
MonotocityNot monotonic
2022-02-19T00:05:36.650410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
02119
99.7%
0.65805877111
 
< 0.1%
0.79719525351
 
< 0.1%
0.70047393361
 
< 0.1%
0.76028806581
 
< 0.1%
0.87043580681
 
< 0.1%
0.84553775741
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
02119
99.7%
0.65805877111
 
< 0.1%
0.70047393361
 
< 0.1%
0.76028806581
 
< 0.1%
0.79719525351
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.87043580681
< 0.1%
0.84553775741
< 0.1%
0.79719525351
< 0.1%
0.76028806581
< 0.1%

nDP
Real number (ℝ≥0)

ZEROS

Distinct169
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0292790213
Minimum0
Maximum1
Zeros1948
Zeros (%)91.6%
Memory size16.7 KiB
2022-02-19T00:05:36.717911image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2877004291
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1084033082
Coefficient of variation (CV)3.702422532
Kurtosis20.0778556
Mean0.0292790213
Median Absolute Deviation (MAD)0
Skewness4.278800677
Sum62.24719928
Variance0.01175127723
MonotocityNot monotonic
2022-02-19T00:05:36.814306image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01948
91.6%
0.15596330284
 
0.2%
0.32521739132
 
0.1%
0.19378238342
 
0.1%
0.3191126282
 
0.1%
0.16389132342
 
0.1%
0.23581342
 
0.1%
0.4293915042
 
0.1%
0.16204506072
 
0.1%
0.70566037741
 
< 0.1%
Other values (159)159
 
7.5%
ValueCountFrequency (%)
01948
91.6%
0.13031358891
 
< 0.1%
0.1481774961
 
< 0.1%
0.14841269841
 
< 0.1%
0.15596330284
 
0.2%
ValueCountFrequency (%)
11
< 0.1%
0.85258358661
< 0.1%
0.84615384621
< 0.1%
0.80835734871
< 0.1%
0.76326530611
< 0.1%

Tendency
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
1115 
1
846 
-1
165 

Length

Max length2
Median length1
Mean length1.077610536
Min length1

Characters and Unicode

Total characters2291
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
01115
52.4%
1846
39.8%
-1165
 
7.8%
2022-02-19T00:05:36.976054image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:37.402865image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
01115
52.4%
11011
47.6%

Most occurring characters

ValueCountFrequency (%)
01115
48.7%
11011
44.1%
-165
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
92.8%
Dash Punctuation165
 
7.2%

Most frequent character per category

ValueCountFrequency (%)
01115
52.4%
11011
47.6%
ValueCountFrequency (%)
-165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2291
100.0%

Most frequent character per script

ValueCountFrequency (%)
01115
48.7%
11011
44.1%
-165
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2291
100.0%

Most frequent character per block

ValueCountFrequency (%)
01115
48.7%
11011
44.1%
-165
 
7.2%

A
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
1742 
1
384 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01742
81.9%
1384
 
18.1%
2022-02-19T00:05:37.516518image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:37.565297image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
01742
81.9%
1384
 
18.1%

Most occurring characters

ValueCountFrequency (%)
01742
81.9%
1384
 
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
01742
81.9%
1384
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
01742
81.9%
1384
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
01742
81.9%
1384
 
18.1%

B
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
1547 
1
579 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
01547
72.8%
1579
 
27.2%
2022-02-19T00:05:37.681593image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:37.734763image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
01547
72.8%
1579
 
27.2%

Most occurring characters

ValueCountFrequency (%)
01547
72.8%
1579
 
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
01547
72.8%
1579
 
27.2%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
01547
72.8%
1579
 
27.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
01547
72.8%
1579
 
27.2%

C
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
2073 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
02073
97.5%
153
 
2.5%
2022-02-19T00:05:37.849562image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:37.897778image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
02073
97.5%
153
 
2.5%

Most occurring characters

ValueCountFrequency (%)
02073
97.5%
153
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
02073
97.5%
153
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
02073
97.5%
153
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
02073
97.5%
153
 
2.5%

D
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
2045 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
02045
96.2%
181
 
3.8%
2022-02-19T00:05:38.025208image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:38.073119image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
02045
96.2%
181
 
3.8%

Most occurring characters

ValueCountFrequency (%)
02045
96.2%
181
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
02045
96.2%
181
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
02045
96.2%
181
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
02045
96.2%
181
 
3.8%

E
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
2054 
1
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
02054
96.6%
172
 
3.4%
2022-02-19T00:05:38.207281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:38.262484image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
02054
96.6%
172
 
3.4%

Most occurring characters

ValueCountFrequency (%)
02054
96.6%
172
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
02054
96.6%
172
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
02054
96.6%
172
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
02054
96.6%
172
 
3.4%

AD
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
1794 
1
332 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0
ValueCountFrequency (%)
01794
84.4%
1332
 
15.6%
2022-02-19T00:05:38.365512image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:38.413587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
01794
84.4%
1332
 
15.6%

Most occurring characters

ValueCountFrequency (%)
01794
84.4%
1332
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
01794
84.4%
1332
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
01794
84.4%
1332
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
01794
84.4%
1332
 
15.6%

DE
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
1874 
1
252 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01874
88.1%
1252
 
11.9%
2022-02-19T00:05:38.542277image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:38.574155image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
01874
88.1%
1252
 
11.9%

Most occurring characters

ValueCountFrequency (%)
01874
88.1%
1252
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
01874
88.1%
1252
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
01874
88.1%
1252
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
01874
88.1%
1252
 
11.9%

LD
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
2019 
1
 
107

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
02019
95.0%
1107
 
5.0%
2022-02-19T00:05:38.701841image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:38.749841image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
02019
95.0%
1107
 
5.0%

Most occurring characters

ValueCountFrequency (%)
02019
95.0%
1107
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
02019
95.0%
1107
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
02019
95.0%
1107
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
02019
95.0%
1107
 
5.0%

FS
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
2057 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
02057
96.8%
169
 
3.2%
2022-02-19T00:05:38.865361image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:38.913637image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
02057
96.8%
169
 
3.2%

Most occurring characters

ValueCountFrequency (%)
02057
96.8%
169
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
02057
96.8%
169
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
02057
96.8%
169
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
02057
96.8%
169
 
3.2%

SUSP
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0
1929 
1
197 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01929
90.7%
1197
 
9.3%
2022-02-19T00:05:39.025396image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:39.073100image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
01929
90.7%
1197
 
9.3%

Most occurring characters

ValueCountFrequency (%)
01929
90.7%
1197
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
01929
90.7%
1197
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
01929
90.7%
1197
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
01929
90.7%
1197
 
9.3%

CLASS
Real number (ℝ≥0)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.509877705
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size16.7 KiB
2022-02-19T00:05:39.105216image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.02688289
Coefficient of variation (CV)0.6711673993
Kurtosis-1.229040401
Mean4.509877705
Median Absolute Deviation (MAD)2
Skewness0.3811634115
Sum9588
Variance9.162020032
MonotocityNot monotonic
2022-02-19T00:05:39.168238image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2579
27.2%
1384
18.1%
6332
15.6%
7252
11.9%
10197
 
9.3%
8107
 
5.0%
481
 
3.8%
572
 
3.4%
969
 
3.2%
353
 
2.5%
ValueCountFrequency (%)
1384
18.1%
2579
27.2%
353
 
2.5%
481
 
3.8%
572
 
3.4%
ValueCountFrequency (%)
10197
9.3%
969
 
3.2%
8107
 
5.0%
7252
11.9%
6332
15.6%

NSP
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
1
1655 
2
295 
3
176 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2126
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%
2022-02-19T00:05:39.325948image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
2022-02-19T00:05:39.358884image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Most occurring characters

ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2126
100.0%

Most frequent character per category

ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common2126
100.0%

Most frequent character per script

ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2126
100.0%

Most frequent character per block

ValueCountFrequency (%)
11655
77.8%
2295
 
13.9%
3176
 
8.3%

Interactions

2022-02-19T00:05:01.165333image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.233270image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.312907image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.376624image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.441177image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.520510image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.584453image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.717791image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.797915image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.863190image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:01.926897image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.007878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.085636image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.167371image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.251649image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.316972image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.416266image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.481669image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.560783image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.626203image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.689646image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.766561image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.833205image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.897443image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:02.962211image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.036041image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.113032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.168890image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.249316image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.317760image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.385091image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.448600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.528154image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.592854image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.738472image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.816917image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.883219image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:03.947685image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.011293image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.090883image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.154692image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.218358image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.298538image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.367692image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.431414image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.495887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.576438image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.639949image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.704309image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.783795image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.847642image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.917709image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:04.997742image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.061725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.125311image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.209144image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.274079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.337571image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.416942image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.485166image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.549008image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.613033image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.693080image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.756699image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.836394image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.900896image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:05.980611image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:06.128906image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:06.208451image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-19T00:05:06.272124image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2022-02-19T00:05:39.438997image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-19T00:05:39.631990image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-19T00:05:39.809484image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-19T00:05:40.012860image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-02-19T00:05:40.180648image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-02-19T00:05:32.477434image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-19T00:05:33.034397image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

LBASTVMSTVALTVMLTVWidthMinMaxNmaxNzerosModeMeanMedianVariancenACnFMnUCnDLnDSnDPTendencyABCDEADDELDFSSUSPCLASSNSP
00.2592590.8133330.0441180.4725270.0473370.3446330.1100920.0344830.1111110.00.4724410.5871560.4036700.2713750.0000000.00.0000000.0000000.00.0000001000000001092
10.4814810.0666670.2794120.0000000.2051280.7175140.1651380.6551720.3333330.10.6377950.5779820.5779820.0446100.3308270.00.4274320.2073370.00.0000000000001000061
20.5000000.0533330.2794120.0000000.2643000.7175140.1651380.6551720.2777780.10.6377950.5688070.5596330.0483270.1722830.00.5564780.2159470.00.0000000000001000061
30.5185190.0533330.3235290.0000000.4536490.6440680.0275230.4137930.6111110.00.6062990.5596330.5504590.0483270.1327970.00.5147250.1664530.00.0000001000001000061
40.4814810.0533330.3235290.0000000.3925050.6440680.0275230.4137930.5000000.00.6062990.5779820.5596330.0408920.3378320.00.5456030.0000000.00.0000001010000000021
50.5185190.1866670.8382350.0000000.0000000.8305080.0000000.6724140.2777780.30.1259840.3119270.2752290.6319700.0544150.00.7030430.6138510.00.3924450000000010083
60.5185190.2266670.8970590.0000000.0000000.8305080.0000000.6724140.3333330.30.0866140.3119270.2660550.7992570.0727310.00.8457220.5469850.00.5245440000000010083
70.2962960.9466670.0441180.0659340.3076920.3672320.1100920.0689660.0000000.00.4881890.4495410.4220180.0111520.0000000.00.0000000.0000000.00.0000001000000001093
80.2962960.9600000.0441180.0549450.2682450.3672320.1100920.0689660.0000000.00.4881890.4495410.4220180.0111520.0000000.00.1016690.0000000.00.0000001000000001093
90.2962960.9866670.0147060.0659340.2090730.3672320.1100920.0689660.0555560.00.4881890.4495410.4220180.0037170.0000000.00.1988130.0000000.00.0000001000000001093

Last rows

LBASTVMSTVALTVMLTVWidthMinMaxNmaxNzerosModeMeanMedianVariancenACnFMnUCnDLnDSnDPTendencyABCDEADDELDFSSUSPCLASSNSP
21160.6296300.9066670.0000000.3956040.0433930.0847460.8256880.3103450.0555560.00.6850390.6880730.6605510.0037170.2057820.0000000.2658730.0000000.00.00010000000021
21170.6296300.8933330.0147060.2197800.1676530.1299430.6788990.2413790.0555560.00.6614170.6422020.6238530.0037170.0000000.0000000.5234380.0000000.00.01100000000011
21180.6296300.8933330.0441180.2857140.1380670.1016950.7247710.2413790.0555560.00.6692910.6330270.6238530.0074350.0000000.0000000.4334570.0600740.00.01100000000011
21190.6296300.8933330.0588240.2967030.1262330.1299430.6788990.2413790.0555560.00.6614170.6238530.6238530.0037170.0000000.0000000.4532130.0732810.00.01100000000011
21200.6296300.8666670.0735290.1868130.1183430.1581920.6788990.2844830.1111110.00.6692910.6422020.6238530.0074350.0000000.0000000.3333330.0808460.00.00100000000011
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21220.6296300.8800000.0294120.2417580.1400390.3559320.4862390.4051720.3333330.00.7244090.6880730.6788990.0111520.0401680.0000000.4670800.0000000.00.01000010000052
21230.6296300.8933330.0294120.2197800.1203160.3615820.4862390.4137930.2777780.00.7322830.6880730.6880730.0148700.0508400.0000000.4598040.0000000.00.01000010000052
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Duplicate rows

Most frequent

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10.3148150.4933330.0882350.0769230.2721890.4011300.1192660.1293100.1111110.00.5433070.4954130.4770640.0074350.0000000.0000000.0000000.00.00.011000000000112
20.3148150.5066670.1029410.0439560.2919130.4463280.0733940.1551720.3888890.00.5433070.5045870.4862390.0185870.1379180.0083000.0000000.00.00.010100000000212
30.3148150.5333330.0882350.0219780.3037480.4915250.0000000.1551720.3888890.00.5433070.5045870.4862390.0148700.1297510.0069410.0000000.00.00.010100000000212
40.5370370.6666670.0441180.7802200.1360950.5310730.1926610.3965520.1666670.00.6535430.6330280.6146790.0037170.0000000.0000000.0000000.00.00.010000000010932
50.6296300.2933330.1470590.0000000.2031560.3220340.6330280.4913790.1111110.00.7559060.7339450.7155960.0185870.3774170.0000000.2925760.00.00.000100000000212
60.7037040.8533330.0294120.6703300.2090730.4406780.1926610.2586210.1666670.00.6692910.6513760.6330280.0074350.0000000.0385770.0000000.00.00.0100000000011022
70.7222220.8666670.0000000.4945050.1143980.1016950.7247710.2413790.0555560.00.6771650.6605500.6422020.0000000.0000000.0421300.0000000.00.00.0100000000011022
80.7407410.7066670.0294120.4285710.1380670.0903950.7981650.2931030.0555560.00.7086610.6972480.6788990.0037170.0000000.0000000.1915650.00.00.0100000000011022
90.7777780.3733330.1029410.0000000.2090730.1807910.7889910.4224140.0555560.00.7322830.7522940.7247710.0148700.2446090.0000000.1580190.00.00.000100000000212